Multiagent Learning during On-Going Human-Machine Interactions: The Role of Reputation

Jacob W. Crandall and Michael A. Goodrich

Multiagent learning is an important tool for long-lasting human-machine systems (HMS). Most multiagent learning algorithms to date have focused on learning a best response to the strategies of other agents in the system. While such an approach is acceptable in some domains, it is not successful in others, such as when humans and machines interact in social dilemma-like situations, such as those arising when human attention is a scarce resource shared by multiple agents. In this paper, we discuss and show (through a user study) how multiagent learning algorithms must be aware of reputational equilibrium in order to establish neglect tolerant interactions.

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